International Journal of u- and e-Service, Science and Technology v. The formula to calculate the user preference of product is shown in expression (3) my indicates the similarity between item j in the individual i and the item previously favored. A is a constant value for normalization P)=∑λxsm The overall evaluation function is the expression (4) And WIN, Ws, Wp represents the weight of each evaluation criteria to maintain the sum makes 3. In the early time with the value of 1: 1: 1, but as the intention of the current user changes, the goal of criteria also changes for making the adapted recommendation. In addition, the constant value of a B, and A should be set differently depending on the policy, in this paper the values were set to 1: 1: 1 f(=ONxN(O+Oss(0)+OpxP( As mentioned in Section 2, the goal of the user is divided into 3 kinds of state: the user does not have special purpose, the user is interested in a particular category, and the user is interested in a particular product. If the user does not have the purpose just to see a variety of products, the diversity of the recommend is important to generate a list of recommends. If the user is interested in a specific category, the suitability of a particular category and a variety of products within that category is important. If the user is interested in a specific product, the most similar product is included in a list of recommended items. It is hard to catch the purpose of the user at a time, depending on the user's behavior gradually changed for each state, bring the importance of the evaluation function is reflected in the genetic algorithm to generate the appropriate recommendation list for the current situation 5 Evaluation Environments In this paper, we used the data collected from the Internet jewellery shop by 2 months to evaluate it. The data includes the user's behaviour, purchasing information, and click stream records from the web server log file. The number of user is 137. and the number of products is 230. In addition, the number of collected click stream is nearly 35000 Analysis of the user's preferences is based on the behaviour of the user, and each users behaviour is analyzed by the score. In addition, we generate item similarity matrix using Item-to-Item CF technique [7 for referring the fitness function of genetic algorithm In this paper, we evaluated the proposed method in terms of the accuracy and the diversity of the recommendation. We use two measures: the accuracy was measured by the Precision and the diversity was measured by the Item Coverage. The measures were typically used in the evaluation of the recommender systemsThe formula to calculate the user preference of product is shown in expression (3): simij indicates the similarity between item j in the individual i and the item previously favored. λ is a constant value for normalization. ∑ ×= (3) ij iP )( λ sim The overall evaluation function is the expression (4). And ωN, ωS, ωP represents the weight of each evaluation criteria to maintain the sum makes 3. In the early time with the value of 1:1:1, but as the intention of the current user changes, the goal of criteria also changes for making the adapted recommendation. In addition, the constant value of α, β, and λ should be set differently depending on the policy, in this paper the values were set to 1:1:1. iPiSiNif )()()()( = ωN × +ωS × +ωP × (4) As mentioned in Section 2, the goal of the user is divided into 3 kinds of state: the user does not have special purpose, the user is interested in a particular category, and the user is interested in a particular product. If the user does not have the purpose just to see a variety of products, the diversity of the recommend is important to generate a list of recommends. If the user is interested in a specific category, the suitability of a particular category and a variety of products within that category is important. If the user is interested in a specific product, the most similar product is included in a list of recommended items. It is hard to catch the purpose of the user at a time, depending on the user's behavior gradually changed for each state, bring the importance of the evaluation function is reflected in the genetic algorithm to generate the appropriate recommendation list for the current situation. 5 Evaluation Environments In this paper, we used the data collected from the Internet jewellery shop by 2 months to evaluate it. The data includes the user's behaviour, purchasing information, and click stream records from the web server log file. The number of user is 137, and the number of products is 230. In addition, the number of collected click stream is nearly 35000. Analysis of the user's preferences is based on the behaviour of the user, and each user's behaviour is analyzed by the score. In addition, we generate item similarity matrix using Item-to-Item CF technique [7] for referring the fitness function of genetic algorithm. In this paper, we evaluated the proposed method in terms of the accuracy and the diversity of the recommendation. We use two measures: the accuracy was measured by the Precision and the diversity was measured by the Item Coverage. The measures were typically used in the evaluation of the recommender systems. 14 International Journal of u- and e- Service, Science and Technology